Overview

Dataset statistics

Number of variables44
Number of observations20000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory352.0 B

Variable types

Numeric44

Alerts

HR is highly correlated with O2Sat and 24 other fieldsHigh correlation
O2Sat is highly correlated with HR and 24 other fieldsHigh correlation
Temp is highly correlated with HR and 19 other fieldsHigh correlation
SBP is highly correlated with HR and 24 other fieldsHigh correlation
MAP is highly correlated with HR and 24 other fieldsHigh correlation
DBP is highly correlated with HR and 24 other fieldsHigh correlation
Resp is highly correlated with HR and 24 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
FiO2 is highly correlated with pH and 6 other fieldsHigh correlation
pH is highly correlated with FiO2 and 6 other fieldsHigh correlation
PaCO2 is highly correlated with FiO2 and 6 other fieldsHigh correlation
SaO2 is highly correlated with FiO2 and 4 other fieldsHigh correlation
AST is highly correlated with Alkalinephos and 1 other fieldsHigh correlation
BUN is highly correlated with HR and 22 other fieldsHigh correlation
Alkalinephos is highly correlated with AST and 1 other fieldsHigh correlation
Calcium is highly correlated with HR and 26 other fieldsHigh correlation
Chloride is highly correlated with LactateHigh correlation
Creatinine is highly correlated with HR and 22 other fieldsHigh correlation
Glucose is highly correlated with HR and 19 other fieldsHigh correlation
Lactate is highly correlated with FiO2 and 7 other fieldsHigh correlation
Magnesium is highly correlated with HR and 22 other fieldsHigh correlation
Phosphate is highly correlated with BUN and 2 other fieldsHigh correlation
Potassium is highly correlated with HR and 27 other fieldsHigh correlation
Bilirubin_total is highly correlated with AST and 1 other fieldsHigh correlation
Hct is highly correlated with HR and 22 other fieldsHigh correlation
Hgb is highly correlated with HR and 22 other fieldsHigh correlation
PTT is highly correlated with FibrinogenHigh correlation
WBC is highly correlated with HR and 22 other fieldsHigh correlation
Fibrinogen is highly correlated with PTTHigh correlation
Platelets is highly correlated with HR and 22 other fieldsHigh correlation
Age is highly correlated with HR and 23 other fieldsHigh correlation
Gender is highly correlated with HR and 23 other fieldsHigh correlation
Unit1 is highly correlated with HR and 13 other fieldsHigh correlation
Unit2 is highly correlated with HR and 13 other fieldsHigh correlation
HospAdmTime is highly correlated with HR and 23 other fieldsHigh correlation
ICULOS is highly correlated with HR and 23 other fieldsHigh correlation
SepsisLabel is highly correlated with HR and 23 other fieldsHigh correlation
Sepsis is highly correlated with HR and 23 other fieldsHigh correlation
Hours is highly correlated with HR and 23 other fieldsHigh correlation
HR is highly correlated with O2Sat and 25 other fieldsHigh correlation
O2Sat is highly correlated with HR and 25 other fieldsHigh correlation
Temp is highly correlated with HR and 28 other fieldsHigh correlation
SBP is highly correlated with HR and 25 other fieldsHigh correlation
MAP is highly correlated with HR and 25 other fieldsHigh correlation
DBP is highly correlated with HR and 25 other fieldsHigh correlation
Resp is highly correlated with HR and 25 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 4 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
FiO2 is highly correlated with Temp and 8 other fieldsHigh correlation
pH is highly correlated with Temp and 11 other fieldsHigh correlation
PaCO2 is highly correlated with Temp and 11 other fieldsHigh correlation
SaO2 is highly correlated with FiO2 and 6 other fieldsHigh correlation
AST is highly correlated with BUN and 5 other fieldsHigh correlation
BUN is highly correlated with HR and 28 other fieldsHigh correlation
Alkalinephos is highly correlated with AST and 5 other fieldsHigh correlation
Calcium is highly correlated with HR and 30 other fieldsHigh correlation
Chloride is highly correlated with BaseExcess and 6 other fieldsHigh correlation
Creatinine is highly correlated with HR and 28 other fieldsHigh correlation
Glucose is highly correlated with HR and 30 other fieldsHigh correlation
Lactate is highly correlated with Temp and 10 other fieldsHigh correlation
Magnesium is highly correlated with HR and 25 other fieldsHigh correlation
Phosphate is highly correlated with HR and 22 other fieldsHigh correlation
Potassium is highly correlated with HR and 31 other fieldsHigh correlation
Bilirubin_total is highly correlated with AST and 5 other fieldsHigh correlation
Hct is highly correlated with HR and 27 other fieldsHigh correlation
Hgb is highly correlated with HR and 27 other fieldsHigh correlation
PTT is highly correlated with FibrinogenHigh correlation
WBC is highly correlated with HR and 28 other fieldsHigh correlation
Fibrinogen is highly correlated with Hct and 2 other fieldsHigh correlation
Platelets is highly correlated with HR and 29 other fieldsHigh correlation
Age is highly correlated with HR and 25 other fieldsHigh correlation
Gender is highly correlated with HR and 25 other fieldsHigh correlation
Unit1 is highly correlated with HR and 23 other fieldsHigh correlation
Unit2 is highly correlated with HR and 23 other fieldsHigh correlation
HospAdmTime is highly correlated with HR and 25 other fieldsHigh correlation
ICULOS is highly correlated with HR and 25 other fieldsHigh correlation
SepsisLabel is highly correlated with HR and 25 other fieldsHigh correlation
Sepsis is highly correlated with HR and 25 other fieldsHigh correlation
Hours is highly correlated with HR and 25 other fieldsHigh correlation
HR is highly correlated with O2Sat and 18 other fieldsHigh correlation
O2Sat is highly correlated with HR and 14 other fieldsHigh correlation
Temp is highly correlated with HR and 11 other fieldsHigh correlation
SBP is highly correlated with HR and 16 other fieldsHigh correlation
MAP is highly correlated with HR and 16 other fieldsHigh correlation
DBP is highly correlated with HR and 16 other fieldsHigh correlation
Resp is highly correlated with HR and 11 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
FiO2 is highly correlated with pH and 4 other fieldsHigh correlation
pH is highly correlated with FiO2 and 3 other fieldsHigh correlation
PaCO2 is highly correlated with FiO2 and 3 other fieldsHigh correlation
SaO2 is highly correlated with FiO2 and 3 other fieldsHigh correlation
AST is highly correlated with Alkalinephos and 1 other fieldsHigh correlation
BUN is highly correlated with HR and 19 other fieldsHigh correlation
Alkalinephos is highly correlated with AST and 1 other fieldsHigh correlation
Calcium is highly correlated with BUN and 9 other fieldsHigh correlation
Chloride is highly correlated with LactateHigh correlation
Creatinine is highly correlated with HR and 19 other fieldsHigh correlation
Glucose is highly correlated with BUN and 3 other fieldsHigh correlation
Lactate is highly correlated with FiO2 and 6 other fieldsHigh correlation
Magnesium is highly correlated with BUN and 8 other fieldsHigh correlation
Phosphate is highly correlated with MagnesiumHigh correlation
Potassium is highly correlated with FiO2 and 10 other fieldsHigh correlation
Bilirubin_total is highly correlated with AST and 1 other fieldsHigh correlation
Hct is highly correlated with BUN and 7 other fieldsHigh correlation
Hgb is highly correlated with BUN and 7 other fieldsHigh correlation
PTT is highly correlated with FibrinogenHigh correlation
WBC is highly correlated with HR and 8 other fieldsHigh correlation
Fibrinogen is highly correlated with PTTHigh correlation
Platelets is highly correlated with HR and 8 other fieldsHigh correlation
Age is highly correlated with HR and 16 other fieldsHigh correlation
Gender is highly correlated with HR and 16 other fieldsHigh correlation
Unit1 is highly correlated with HR and 12 other fieldsHigh correlation
Unit2 is highly correlated with HR and 12 other fieldsHigh correlation
HospAdmTime is highly correlated with HR and 16 other fieldsHigh correlation
ICULOS is highly correlated with HR and 16 other fieldsHigh correlation
SepsisLabel is highly correlated with HR and 16 other fieldsHigh correlation
Sepsis is highly correlated with HR and 16 other fieldsHigh correlation
Hours is highly correlated with HR and 16 other fieldsHigh correlation
PatientID is uniformly distributed Uniform
PatientID has unique values Unique
EtCO2 has 16784 (83.9%) zeros Zeros
BaseExcess has 19442 (97.2%) zeros Zeros
HCO3 has 19584 (97.9%) zeros Zeros
FiO2 has 14178 (70.9%) zeros Zeros
pH has 14246 (71.2%) zeros Zeros
PaCO2 has 14221 (71.1%) zeros Zeros
SaO2 has 14875 (74.4%) zeros Zeros
AST has 11536 (57.7%) zeros Zeros
BUN has 1591 (8.0%) zeros Zeros
Alkalinephos has 11530 (57.6%) zeros Zeros
Calcium has 1550 (7.8%) zeros Zeros
Chloride has 18383 (91.9%) zeros Zeros
Creatinine has 1588 (7.9%) zeros Zeros
Bilirubin_direct has 18529 (92.6%) zeros Zeros
Glucose has 1173 (5.9%) zeros Zeros
Lactate has 15240 (76.2%) zeros Zeros
Magnesium has 3543 (17.7%) zeros Zeros
Phosphate has 8365 (41.8%) zeros Zeros
Potassium has 1434 (7.2%) zeros Zeros
Bilirubin_total has 11522 (57.6%) zeros Zeros
TroponinI has 13436 (67.2%) zeros Zeros
Hct has 1953 (9.8%) zeros Zeros
Hgb has 1941 (9.7%) zeros Zeros
PTT has 15602 (78.0%) zeros Zeros
WBC has 2000 (10.0%) zeros Zeros
Fibrinogen has 18052 (90.3%) zeros Zeros
Platelets has 1992 (10.0%) zeros Zeros
Unit1 has 6095 (30.5%) zeros Zeros
Unit2 has 6095 (30.5%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:24:07.469039
Analysis finished2021-11-29 10:24:23.465570
Duration16 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110000.5
Minimum100001
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:23.514746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile101000.95
Q1105000.75
median110000.5
Q3115000.25
95-th percentile119000.05
Maximum120000
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.05248746167
Kurtosis-1.2
Mean110000.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum2200010000
Variance33335000
MonotonicityStrictly increasing
2021-11-29T11:24:23.622694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000011
 
< 0.1%
1133311
 
< 0.1%
1133381
 
< 0.1%
1133371
 
< 0.1%
1133361
 
< 0.1%
1133351
 
< 0.1%
1133341
 
< 0.1%
1133331
 
< 0.1%
1133321
 
< 0.1%
1133301
 
< 0.1%
Other values (19990)19990
> 99.9%
ValueCountFrequency (%)
1000011
< 0.1%
1000021
< 0.1%
1000031
< 0.1%
1000041
< 0.1%
1000051
< 0.1%
1000061
< 0.1%
1000071
< 0.1%
1000081
< 0.1%
1000091
< 0.1%
1000101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct231
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.48925
Minimum0
Maximum328
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:23.736400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q119
median34
Q343
95-th percentile55
Maximum328
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.59715504
Coefficient of variation (CV)0.6747584685
Kurtosis38.19256726
Mean33.48925
Median Absolute Deviation (MAD)12
Skewness4.457245692
Sum669785
Variance510.631416
MonotonicityNot monotonic
2021-11-29T11:24:23.909419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35648
 
3.2%
39620
 
3.1%
38610
 
3.0%
37596
 
3.0%
36595
 
3.0%
40585
 
2.9%
41545
 
2.7%
42532
 
2.7%
34526
 
2.6%
43492
 
2.5%
Other values (221)14251
71.3%
ValueCountFrequency (%)
04
 
< 0.1%
17
 
< 0.1%
240
 
0.2%
3118
0.6%
4202
1.0%
5199
1.0%
6195
1.0%
7267
1.3%
8197
1.0%
9202
1.0%
ValueCountFrequency (%)
3283
< 0.1%
3241
 
< 0.1%
3181
 
< 0.1%
3121
 
< 0.1%
3111
 
< 0.1%
3081
 
< 0.1%
3071
 
< 0.1%
3061
 
< 0.1%
3011
 
< 0.1%
2972
< 0.1%

O2Sat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.7169
Minimum0
Maximum328
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:24.014481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q119
median33
Q342
95-th percentile54
Maximum328
Range328
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.45235087
Coefficient of variation (CV)0.6862615613
Kurtosis37.59430454
Mean32.7169
Median Absolute Deviation (MAD)12
Skewness4.388133365
Sum654338
Variance504.1080598
MonotonicityNot monotonic
2021-11-29T11:24:24.113253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35633
 
3.2%
39606
 
3.0%
36585
 
2.9%
37568
 
2.8%
38562
 
2.8%
40544
 
2.7%
42525
 
2.6%
34515
 
2.6%
21514
 
2.6%
19514
 
2.6%
Other values (219)14434
72.2%
ValueCountFrequency (%)
06
 
< 0.1%
112
 
0.1%
258
 
0.3%
3140
0.7%
4219
1.1%
5232
1.2%
6200
1.0%
7292
1.5%
8215
1.1%
9229
1.1%
ValueCountFrequency (%)
3281
< 0.1%
3271
< 0.1%
3251
< 0.1%
3231
< 0.1%
3181
< 0.1%
3121
< 0.1%
3101
< 0.1%
3081
< 0.1%
3041
< 0.1%
3021
< 0.1%

Temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.91625
Minimum0
Maximum214
Zeros49
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:24.220041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median9
Q314
95-th percentile39
Maximum214
Range214
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.0363121
Coefficient of variation (CV)1.00929543
Kurtosis37.34657591
Mean12.91625
Median Absolute Deviation (MAD)4
Skewness4.435887039
Sum258325
Variance169.9454332
MonotonicityNot monotonic
2021-11-29T11:24:24.314029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91617
 
8.1%
101448
 
7.2%
51418
 
7.1%
81372
 
6.9%
41356
 
6.8%
111352
 
6.8%
71224
 
6.1%
61214
 
6.1%
121031
 
5.2%
3976
 
4.9%
Other values (125)6992
35.0%
ValueCountFrequency (%)
049
 
0.2%
1229
 
1.1%
2599
 
3.0%
3976
4.9%
41356
6.8%
51418
7.1%
61214
6.1%
71224
6.1%
81372
6.9%
91617
8.1%
ValueCountFrequency (%)
2141
< 0.1%
2131
< 0.1%
2061
< 0.1%
2051
< 0.1%
1962
< 0.1%
1951
< 0.1%
1941
< 0.1%
1801
< 0.1%
1671
< 0.1%
1602
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct236
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.79655
Minimum0
Maximum332
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:24.411895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q119
median33
Q342
95-th percentile54
Maximum332
Range332
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.43228072
Coefficient of variation (CV)0.683982941
Kurtosis38.11703274
Mean32.79655
Median Absolute Deviation (MAD)12
Skewness4.442117865
Sum655931
Variance503.2072185
MonotonicityNot monotonic
2021-11-29T11:24:24.510407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35632
 
3.2%
37624
 
3.1%
38585
 
2.9%
36582
 
2.9%
39582
 
2.9%
40553
 
2.8%
42535
 
2.7%
34523
 
2.6%
41514
 
2.6%
19498
 
2.5%
Other values (226)14372
71.9%
ValueCountFrequency (%)
024
 
0.1%
114
 
0.1%
241
 
0.2%
3135
0.7%
4216
1.1%
5217
1.1%
6188
0.9%
7283
1.4%
8213
1.1%
9222
1.1%
ValueCountFrequency (%)
3321
< 0.1%
3261
< 0.1%
3251
< 0.1%
3221
< 0.1%
3121
< 0.1%
3111
< 0.1%
3061
< 0.1%
3051
< 0.1%
3041
< 0.1%
3031
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct232
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.47915
Minimum0
Maximum332
Zeros102
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:24.614223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q119
median33
Q342
95-th percentile54
Maximum332
Range332
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.58490135
Coefficient of variation (CV)0.6953661458
Kurtosis36.93726161
Mean32.47915
Median Absolute Deviation (MAD)12
Skewness4.328911807
Sum649583
Variance510.0777692
MonotonicityNot monotonic
2021-11-29T11:24:24.712004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35629
 
3.1%
37627
 
3.1%
36584
 
2.9%
38583
 
2.9%
39573
 
2.9%
40558
 
2.8%
42532
 
2.7%
34514
 
2.6%
19511
 
2.6%
20489
 
2.4%
Other values (222)14400
72.0%
ValueCountFrequency (%)
0102
 
0.5%
1118
0.6%
2158
0.8%
3196
1.0%
4190
0.9%
5170
0.9%
6167
0.8%
7285
1.4%
8194
1.0%
9192
1.0%
ValueCountFrequency (%)
3321
< 0.1%
3261
< 0.1%
3241
< 0.1%
3221
< 0.1%
3112
< 0.1%
3051
< 0.1%
3041
< 0.1%
3021
< 0.1%
3001
< 0.1%
2991
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct234
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.7869
Minimum0
Maximum332
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:24.817420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q119
median33
Q342
95-th percentile54
Maximum332
Range332
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.4326425
Coefficient of variation (CV)0.6841952884
Kurtosis38.13152792
Mean32.7869
Median Absolute Deviation (MAD)12
Skewness4.443233746
Sum655738
Variance503.2234496
MonotonicityNot monotonic
2021-11-29T11:24:24.915309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35628
 
3.1%
37619
 
3.1%
38592
 
3.0%
39581
 
2.9%
36580
 
2.9%
40553
 
2.8%
42536
 
2.7%
34528
 
2.6%
41509
 
2.5%
19498
 
2.5%
Other values (224)14376
71.9%
ValueCountFrequency (%)
027
 
0.1%
112
 
0.1%
240
 
0.2%
3136
0.7%
4217
1.1%
5218
1.1%
6186
0.9%
7283
1.4%
8214
1.1%
9222
1.1%
ValueCountFrequency (%)
3321
< 0.1%
3261
< 0.1%
3251
< 0.1%
3221
< 0.1%
3121
< 0.1%
3111
< 0.1%
3061
< 0.1%
3051
< 0.1%
3041
< 0.1%
3031
< 0.1%

Resp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct225
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.0459
Minimum0
Maximum327
Zeros43
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:25.019088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q117
median29
Q339
95-th percentile52
Maximum327
Range327
Interquartile range (IQR)22

Descriptive statistics

Standard deviation20.9401604
Coefficient of variation (CV)0.6969390301
Kurtosis37.85055592
Mean30.0459
Median Absolute Deviation (MAD)11
Skewness4.353968692
Sum600918
Variance438.4903177
MonotonicityNot monotonic
2021-11-29T11:24:25.116727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35577
 
2.9%
36558
 
2.8%
37550
 
2.8%
39540
 
2.7%
38538
 
2.7%
17536
 
2.7%
33534
 
2.7%
18528
 
2.6%
19527
 
2.6%
20517
 
2.6%
Other values (215)14595
73.0%
ValueCountFrequency (%)
043
 
0.2%
142
 
0.2%
265
 
0.3%
3183
0.9%
4262
1.3%
5260
1.3%
6246
1.2%
7297
1.5%
8253
1.3%
9284
1.4%
ValueCountFrequency (%)
3271
< 0.1%
3151
< 0.1%
3001
< 0.1%
2981
< 0.1%
2971
< 0.1%
2881
< 0.1%
2851
< 0.1%
2842
< 0.1%
2801
< 0.1%
2771
< 0.1%

EtCO2
Real number (ℝ≥0)

ZEROS

Distinct130
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8818
Minimum0
Maximum308
Zeros16784
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:25.295244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18
Maximum308
Range308
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.98002107
Coefficient of variation (CV)4.157131331
Kurtosis151.1111183
Mean2.8818
Median Absolute Deviation (MAD)0
Skewness9.799449423
Sum57636
Variance143.5209048
MonotonicityNot monotonic
2021-11-29T11:24:25.392782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016784
83.9%
2284
 
1.4%
1231
 
1.2%
3221
 
1.1%
4178
 
0.9%
6154
 
0.8%
5143
 
0.7%
7122
 
0.6%
897
 
0.5%
1193
 
0.5%
Other values (120)1693
 
8.5%
ValueCountFrequency (%)
016784
83.9%
1231
 
1.2%
2284
 
1.4%
3221
 
1.1%
4178
 
0.9%
5143
 
0.7%
6154
 
0.8%
7122
 
0.6%
897
 
0.5%
975
 
0.4%
ValueCountFrequency (%)
3081
< 0.1%
2931
< 0.1%
2871
< 0.1%
2551
< 0.1%
2461
< 0.1%
2381
< 0.1%
2251
< 0.1%
2221
< 0.1%
2201
< 0.1%
2171
< 0.1%

BaseExcess
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0882
Minimum0
Maximum20
Zeros19442
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:25.484114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6766414502
Coefficient of variation (CV)7.671671771
Kurtosis145.8723544
Mean0.0882
Median Absolute Deviation (MAD)0
Skewness10.67402605
Sum1764
Variance0.4578436522
MonotonicityNot monotonic
2021-11-29T11:24:25.562077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
019442
97.2%
1213
 
1.1%
290
 
0.4%
457
 
0.3%
557
 
0.3%
350
 
0.2%
635
 
0.2%
816
 
0.1%
714
 
0.1%
910
 
0.1%
Other values (6)16
 
0.1%
ValueCountFrequency (%)
019442
97.2%
1213
 
1.1%
290
 
0.4%
350
 
0.2%
457
 
0.3%
557
 
0.3%
635
 
0.2%
714
 
0.1%
816
 
0.1%
910
 
0.1%
ValueCountFrequency (%)
201
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
113
 
< 0.1%
109
 
< 0.1%
910
 
0.1%
816
0.1%
714
 
0.1%
635
0.2%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07055
Minimum0
Maximum20
Zeros19584
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:25.649151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6250537834
Coefficient of variation (CV)8.859727617
Kurtosis181.6938622
Mean0.07055
Median Absolute Deviation (MAD)0
Skewness12.01682714
Sum1411
Variance0.3906922321
MonotonicityNot monotonic
2021-11-29T11:24:25.727063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
019584
97.9%
1152
 
0.8%
261
 
0.3%
443
 
0.2%
543
 
0.2%
334
 
0.2%
630
 
0.1%
814
 
0.1%
714
 
0.1%
910
 
0.1%
Other values (5)15
 
0.1%
ValueCountFrequency (%)
019584
97.9%
1152
 
0.8%
261
 
0.3%
334
 
0.2%
443
 
0.2%
543
 
0.2%
630
 
0.1%
714
 
0.1%
814
 
0.1%
910
 
0.1%
ValueCountFrequency (%)
201
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
113
 
< 0.1%
109
 
< 0.1%
910
 
0.1%
814
 
0.1%
714
 
0.1%
630
0.1%
543
0.2%

FiO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8605
Minimum0
Maximum60
Zeros14178
Zeros (%)70.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:25.821848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum60
Range60
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.332897741
Coefficient of variation (CV)2.711095574
Kurtosis103.5663008
Mean0.8605
Median Absolute Deviation (MAD)0
Skewness7.52012698
Sum17210
Variance5.442411871
MonotonicityNot monotonic
2021-11-29T11:24:25.913755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
014178
70.9%
12449
 
12.2%
21179
 
5.9%
3764
 
3.8%
4453
 
2.3%
5282
 
1.4%
6181
 
0.9%
7133
 
0.7%
877
 
0.4%
966
 
0.3%
Other values (31)238
 
1.2%
ValueCountFrequency (%)
014178
70.9%
12449
 
12.2%
21179
 
5.9%
3764
 
3.8%
4453
 
2.3%
5282
 
1.4%
6181
 
0.9%
7133
 
0.7%
877
 
0.4%
966
 
0.3%
ValueCountFrequency (%)
601
< 0.1%
561
< 0.1%
491
< 0.1%
451
< 0.1%
441
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%
362
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8479
Minimum0
Maximum64
Zeros14246
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:26.012070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.379737117
Coefficient of variation (CV)2.80662474
Kurtosis123.2716219
Mean0.8479
Median Absolute Deviation (MAD)0
Skewness8.1267563
Sum16958
Variance5.663148747
MonotonicityNot monotonic
2021-11-29T11:24:26.102843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
014246
71.2%
12534
 
12.7%
21182
 
5.9%
3655
 
3.3%
4405
 
2.0%
5258
 
1.3%
6174
 
0.9%
7141
 
0.7%
886
 
0.4%
979
 
0.4%
Other values (30)240
 
1.2%
ValueCountFrequency (%)
014246
71.2%
12534
 
12.7%
21182
 
5.9%
3655
 
3.3%
4405
 
2.0%
5258
 
1.3%
6174
 
0.9%
7141
 
0.7%
886
 
0.4%
979
 
0.4%
ValueCountFrequency (%)
641
< 0.1%
582
< 0.1%
501
< 0.1%
481
< 0.1%
461
< 0.1%
431
< 0.1%
421
< 0.1%
392
< 0.1%
361
< 0.1%
341
< 0.1%

PaCO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85065
Minimum0
Maximum64
Zeros14221
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:26.199648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.37257793
Coefficient of variation (CV)2.789135285
Kurtosis120.8458721
Mean0.85065
Median Absolute Deviation (MAD)0
Skewness8.028096653
Sum17013
Variance5.629126034
MonotonicityNot monotonic
2021-11-29T11:24:26.289050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
014221
71.1%
12533
 
12.7%
21198
 
6.0%
3668
 
3.3%
4401
 
2.0%
5255
 
1.3%
6174
 
0.9%
7145
 
0.7%
885
 
0.4%
980
 
0.4%
Other values (28)240
 
1.2%
ValueCountFrequency (%)
014221
71.1%
12533
 
12.7%
21198
 
6.0%
3668
 
3.3%
4401
 
2.0%
5255
 
1.3%
6174
 
0.9%
7145
 
0.7%
885
 
0.4%
980
 
0.4%
ValueCountFrequency (%)
641
< 0.1%
582
< 0.1%
501
< 0.1%
461
< 0.1%
431
< 0.1%
422
< 0.1%
392
< 0.1%
361
< 0.1%
331
< 0.1%
321
< 0.1%

SaO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72005
Minimum0
Maximum64
Zeros14875
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:26.383087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.149583456
Coefficient of variation (CV)2.985325263
Kurtosis159.6088907
Mean0.72005
Median Absolute Deviation (MAD)0
Skewness9.109767191
Sum14401
Variance4.620709033
MonotonicityNot monotonic
2021-11-29T11:24:26.467707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
014875
74.4%
12344
 
11.7%
21025
 
5.1%
3573
 
2.9%
4372
 
1.9%
5242
 
1.2%
6153
 
0.8%
7110
 
0.5%
868
 
0.3%
960
 
0.3%
Other values (24)178
 
0.9%
ValueCountFrequency (%)
014875
74.4%
12344
 
11.7%
21025
 
5.1%
3573
 
2.9%
4372
 
1.9%
5242
 
1.2%
6153
 
0.8%
7110
 
0.5%
868
 
0.3%
960
 
0.3%
ValueCountFrequency (%)
641
 
< 0.1%
582
< 0.1%
501
 
< 0.1%
461
 
< 0.1%
431
 
< 0.1%
392
< 0.1%
361
 
< 0.1%
322
< 0.1%
313
< 0.1%
262
< 0.1%

AST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66815
Minimum0
Maximum30
Zeros11536
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:26.554080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.101991968
Coefficient of variation (CV)1.649318218
Kurtosis70.02718315
Mean0.66815
Median Absolute Deviation (MAD)0
Skewness4.996338343
Sum13363
Variance1.214386297
MonotonicityNot monotonic
2021-11-29T11:24:26.630572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
011536
57.7%
15478
27.4%
21988
 
9.9%
3628
 
3.1%
4179
 
0.9%
583
 
0.4%
648
 
0.2%
723
 
0.1%
89
 
< 0.1%
96
 
< 0.1%
Other values (11)22
 
0.1%
ValueCountFrequency (%)
011536
57.7%
15478
27.4%
21988
 
9.9%
3628
 
3.1%
4179
 
0.9%
583
 
0.4%
648
 
0.2%
723
 
0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
261
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
162
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
132
< 0.1%
124
< 0.1%
114
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1046
Minimum0
Maximum32
Zeros1591
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:26.790019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.740548855
Coefficient of variation (CV)0.8270212177
Kurtosis28.71099364
Mean2.1046
Median Absolute Deviation (MAD)1
Skewness3.687600962
Sum42092
Variance3.029510316
MonotonicityNot monotonic
2021-11-29T11:24:26.871292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26712
33.6%
16121
30.6%
33266
16.3%
01591
 
8.0%
41142
 
5.7%
5484
 
2.4%
6229
 
1.1%
7157
 
0.8%
879
 
0.4%
962
 
0.3%
Other values (17)157
 
0.8%
ValueCountFrequency (%)
01591
 
8.0%
16121
30.6%
26712
33.6%
33266
16.3%
41142
 
5.7%
5484
 
2.4%
6229
 
1.1%
7157
 
0.8%
879
 
0.4%
962
 
0.3%
ValueCountFrequency (%)
321
 
< 0.1%
262
 
< 0.1%
242
 
< 0.1%
233
< 0.1%
221
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
191
 
< 0.1%
185
< 0.1%
176
< 0.1%

Alkalinephos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67045
Minimum0
Maximum30
Zeros11530
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:26.955950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.108651547
Coefficient of variation (CV)1.653593179
Kurtosis68.98757221
Mean0.67045
Median Absolute Deviation (MAD)0
Skewness4.98957423
Sum13409
Variance1.229108253
MonotonicityNot monotonic
2021-11-29T11:24:27.033483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
011530
57.6%
15478
27.4%
21986
 
9.9%
3627
 
3.1%
4184
 
0.9%
583
 
0.4%
649
 
0.2%
724
 
0.1%
810
 
0.1%
96
 
< 0.1%
Other values (11)23
 
0.1%
ValueCountFrequency (%)
011530
57.6%
15478
27.4%
21986
 
9.9%
3627
 
3.1%
4184
 
0.9%
583
 
0.4%
649
 
0.2%
724
 
0.1%
810
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
261
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
162
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
132
 
< 0.1%
125
< 0.1%
114
< 0.1%

Calcium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.60065
Minimum0
Maximum67
Zeros1550
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:27.128731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum67
Range67
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.793234628
Coefficient of variation (CV)1.074052498
Kurtosis75.55357481
Mean2.60065
Median Absolute Deviation (MAD)1
Skewness6.111238499
Sum52013
Variance7.802159685
MonotonicityNot monotonic
2021-11-29T11:24:27.227391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
26075
30.4%
15263
26.3%
33210
16.1%
01550
 
7.8%
41455
 
7.3%
5790
 
4.0%
6501
 
2.5%
7351
 
1.8%
8214
 
1.1%
9146
 
0.7%
Other values (38)445
 
2.2%
ValueCountFrequency (%)
01550
 
7.8%
15263
26.3%
26075
30.4%
33210
16.1%
41455
 
7.3%
5790
 
4.0%
6501
 
2.5%
7351
 
1.8%
8214
 
1.1%
9146
 
0.7%
ValueCountFrequency (%)
671
< 0.1%
621
< 0.1%
521
< 0.1%
502
< 0.1%
482
< 0.1%
471
< 0.1%
441
< 0.1%
411
< 0.1%
391
< 0.1%
381
< 0.1%

Chloride
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23445
Minimum0
Maximum47
Zeros18383
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:27.319193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.258476216
Coefficient of variation (CV)5.367780831
Kurtosis251.7734168
Mean0.23445
Median Absolute Deviation (MAD)0
Skewness11.9612307
Sum4689
Variance1.583762386
MonotonicityNot monotonic
2021-11-29T11:24:27.399249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
018383
91.9%
1785
 
3.9%
2300
 
1.5%
3143
 
0.7%
490
 
0.4%
573
 
0.4%
646
 
0.2%
744
 
0.2%
835
 
0.2%
926
 
0.1%
Other values (14)75
 
0.4%
ValueCountFrequency (%)
018383
91.9%
1785
 
3.9%
2300
 
1.5%
3143
 
0.7%
490
 
0.4%
573
 
0.4%
646
 
0.2%
744
 
0.2%
835
 
0.2%
926
 
0.1%
ValueCountFrequency (%)
471
 
< 0.1%
421
 
< 0.1%
311
 
< 0.1%
292
 
< 0.1%
211
 
< 0.1%
202
 
< 0.1%
193
< 0.1%
162
 
< 0.1%
155
< 0.1%
147
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.10645
Minimum0
Maximum32
Zeros1588
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:27.482610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.742087868
Coefficient of variation (CV)0.8270255018
Kurtosis28.6299876
Mean2.10645
Median Absolute Deviation (MAD)1
Skewness3.684261325
Sum42129
Variance3.034870141
MonotonicityNot monotonic
2021-11-29T11:24:27.563788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26716
33.6%
16114
30.6%
33266
16.3%
01588
 
7.9%
41142
 
5.7%
5489
 
2.4%
6230
 
1.1%
7156
 
0.8%
879
 
0.4%
962
 
0.3%
Other values (17)158
 
0.8%
ValueCountFrequency (%)
01588
 
7.9%
16114
30.6%
26716
33.6%
33266
16.3%
41142
 
5.7%
5489
 
2.4%
6230
 
1.1%
7156
 
0.8%
879
 
0.4%
962
 
0.3%
ValueCountFrequency (%)
321
 
< 0.1%
262
 
< 0.1%
242
 
< 0.1%
233
< 0.1%
221
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
191
 
< 0.1%
185
< 0.1%
176
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0904
Minimum0
Maximum11
Zeros18529
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:27.645101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.375412955
Coefficient of variation (CV)4.152798174
Kurtosis95.25190262
Mean0.0904
Median Absolute Deviation (MAD)0
Skewness7.227513995
Sum1808
Variance0.1409348867
MonotonicityNot monotonic
2021-11-29T11:24:27.713804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
018529
92.6%
11258
 
6.3%
2149
 
0.7%
335
 
0.2%
415
 
0.1%
68
 
< 0.1%
54
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
018529
92.6%
11258
 
6.3%
2149
 
0.7%
335
 
0.2%
415
 
0.1%
54
 
< 0.1%
68
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
81
 
< 0.1%
68
 
< 0.1%
54
 
< 0.1%
415
 
0.1%
335
 
0.2%
2149
 
0.7%
11258
 
6.3%
018529
92.6%

Glucose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct99
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.443
Minimum0
Maximum154
Zeros1173
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:27.802650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q311
95-th percentile25
Maximum154
Range154
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.249028502
Coefficient of variation (CV)1.095467074
Kurtosis30.38250452
Mean8.443
Median Absolute Deviation (MAD)4
Skewness3.691869126
Sum168860
Variance85.54452823
MonotonicityNot monotonic
2021-11-29T11:24:27.902864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22281
 
11.4%
12072
 
10.4%
31548
 
7.7%
41332
 
6.7%
01173
 
5.9%
51103
 
5.5%
9994
 
5.0%
8988
 
4.9%
6961
 
4.8%
7959
 
4.8%
Other values (89)6589
32.9%
ValueCountFrequency (%)
01173
5.9%
12072
10.4%
22281
11.4%
31548
7.7%
41332
6.7%
51103
5.5%
6961
4.8%
7959
4.8%
8988
4.9%
9994
5.0%
ValueCountFrequency (%)
1541
< 0.1%
1511
< 0.1%
1472
< 0.1%
1441
< 0.1%
1401
< 0.1%
1281
< 0.1%
1221
< 0.1%
1191
< 0.1%
1161
< 0.1%
1081
< 0.1%

Lactate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71515
Minimum0
Maximum63
Zeros15240
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:28.006779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.266995245
Coefficient of variation (CV)3.169957694
Kurtosis139.7676586
Mean0.71515
Median Absolute Deviation (MAD)0
Skewness8.827982589
Sum14303
Variance5.139267441
MonotonicityNot monotonic
2021-11-29T11:24:28.098245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
015240
76.2%
12140
 
10.7%
2916
 
4.6%
3550
 
2.8%
4301
 
1.5%
5221
 
1.1%
6146
 
0.7%
7132
 
0.7%
875
 
0.4%
963
 
0.3%
Other values (33)216
 
1.1%
ValueCountFrequency (%)
015240
76.2%
12140
 
10.7%
2916
 
4.6%
3550
 
2.8%
4301
 
1.5%
5221
 
1.1%
6146
 
0.7%
7132
 
0.7%
875
 
0.4%
963
 
0.3%
ValueCountFrequency (%)
631
< 0.1%
561
< 0.1%
551
< 0.1%
501
< 0.1%
471
< 0.1%
461
< 0.1%
431
< 0.1%
421
< 0.1%
392
< 0.1%
361
< 0.1%

Magnesium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8235
Minimum0
Maximum31
Zeros3543
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:28.265730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5
Maximum31
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.784154396
Coefficient of variation (CV)0.9784230307
Kurtosis34.29397116
Mean1.8235
Median Absolute Deviation (MAD)1
Skewness3.901469988
Sum36470
Variance3.18320691
MonotonicityNot monotonic
2021-11-29T11:24:28.344107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
16217
31.1%
25545
27.7%
03543
17.7%
32634
13.2%
41053
 
5.3%
5449
 
2.2%
6207
 
1.0%
7116
 
0.6%
857
 
0.3%
947
 
0.2%
Other values (18)132
 
0.7%
ValueCountFrequency (%)
03543
17.7%
16217
31.1%
25545
27.7%
32634
13.2%
41053
 
5.3%
5449
 
2.2%
6207
 
1.0%
7116
 
0.6%
857
 
0.3%
947
 
0.2%
ValueCountFrequency (%)
311
 
< 0.1%
301
 
< 0.1%
283
< 0.1%
262
 
< 0.1%
241
 
< 0.1%
222
 
< 0.1%
212
 
< 0.1%
203
< 0.1%
195
< 0.1%
186
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.12025
Minimum0
Maximum31
Zeros8365
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:28.426622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.501167084
Coefficient of variation (CV)1.34002864
Kurtosis36.30031125
Mean1.12025
Median Absolute Deviation (MAD)1
Skewness3.905006035
Sum22405
Variance2.253502613
MonotonicityNot monotonic
2021-11-29T11:24:28.505231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
08365
41.8%
15585
27.9%
23878
19.4%
31221
 
6.1%
4433
 
2.2%
5186
 
0.9%
6128
 
0.6%
767
 
0.3%
838
 
0.2%
926
 
0.1%
Other values (14)73
 
0.4%
ValueCountFrequency (%)
08365
41.8%
15585
27.9%
23878
19.4%
31221
 
6.1%
4433
 
2.2%
5186
 
0.9%
6128
 
0.6%
767
 
0.3%
838
 
0.2%
926
 
0.1%
ValueCountFrequency (%)
311
 
< 0.1%
263
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
173
< 0.1%
162
 
< 0.1%
151
 
< 0.1%
145
< 0.1%

Potassium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.93445
Minimum0
Maximum81
Zeros1434
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:28.601718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile8
Maximum81
Range81
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.316580043
Coefficient of variation (CV)1.130222032
Kurtosis73.04387802
Mean2.93445
Median Absolute Deviation (MAD)1
Skewness5.975769523
Sum58689
Variance10.99970318
MonotonicityNot monotonic
2021-11-29T11:24:28.699982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25538
27.7%
14870
24.3%
33319
16.6%
41639
 
8.2%
01434
 
7.2%
5975
 
4.9%
6603
 
3.0%
7459
 
2.3%
8293
 
1.5%
9196
 
1.0%
Other values (40)674
 
3.4%
ValueCountFrequency (%)
01434
 
7.2%
14870
24.3%
25538
27.7%
33319
16.6%
41639
 
8.2%
5975
 
4.9%
6603
 
3.0%
7459
 
2.3%
8293
 
1.5%
9196
 
1.0%
ValueCountFrequency (%)
811
 
< 0.1%
771
 
< 0.1%
661
 
< 0.1%
591
 
< 0.1%
523
< 0.1%
513
< 0.1%
461
 
< 0.1%
432
< 0.1%
421
 
< 0.1%
412
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6724
Minimum0
Maximum30
Zeros11522
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:28.790755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112267997
Coefficient of variation (CV)1.654176081
Kurtosis68.29033032
Mean0.6724
Median Absolute Deviation (MAD)0
Skewness4.974803774
Sum13448
Variance1.237140097
MonotonicityNot monotonic
2021-11-29T11:24:28.867568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
011522
57.6%
15475
27.4%
21989
 
9.9%
3632
 
3.2%
4184
 
0.9%
584
 
0.4%
650
 
0.2%
723
 
0.1%
812
 
0.1%
125
 
< 0.1%
Other values (11)24
 
0.1%
ValueCountFrequency (%)
011522
57.6%
15475
27.4%
21989
 
9.9%
3632
 
3.2%
4184
 
0.9%
584
 
0.4%
650
 
0.2%
723
 
0.1%
812
 
0.1%
95
 
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
261
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
162
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
132
 
< 0.1%
125
< 0.1%
114
< 0.1%

TroponinI
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6908
Minimum0
Maximum17
Zeros13436
Zeros (%)67.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:28.947833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.303564469
Coefficient of variation (CV)1.887036
Kurtosis10.15790562
Mean0.6908
Median Absolute Deviation (MAD)0
Skewness2.698331102
Sum13816
Variance1.699280324
MonotonicityNot monotonic
2021-11-29T11:24:29.022142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
013436
67.2%
13043
 
15.2%
21700
 
8.5%
3879
 
4.4%
4461
 
2.3%
5235
 
1.2%
6117
 
0.6%
777
 
0.4%
827
 
0.1%
914
 
0.1%
Other values (5)11
 
0.1%
ValueCountFrequency (%)
013436
67.2%
13043
 
15.2%
21700
 
8.5%
3879
 
4.4%
4461
 
2.3%
5235
 
1.2%
6117
 
0.6%
777
 
0.4%
827
 
0.1%
914
 
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
142
 
< 0.1%
123
 
< 0.1%
113
 
< 0.1%
102
 
< 0.1%
914
 
0.1%
827
 
0.1%
777
 
0.4%
6117
0.6%
5235
1.2%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.21875
Minimum0
Maximum41
Zeros1953
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:29.112617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum41
Range41
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.064803043
Coefficient of variation (CV)0.9306154562
Kurtosis26.95102252
Mean2.21875
Median Absolute Deviation (MAD)1
Skewness3.566158349
Sum44375
Variance4.263411608
MonotonicityNot monotonic
2021-11-29T11:24:29.195350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
16126
30.6%
26095
30.5%
32880
14.4%
01953
 
9.8%
41152
 
5.8%
5642
 
3.2%
6381
 
1.9%
7235
 
1.2%
8165
 
0.8%
9127
 
0.6%
Other values (21)244
 
1.2%
ValueCountFrequency (%)
01953
 
9.8%
16126
30.6%
26095
30.5%
32880
14.4%
41152
 
5.8%
5642
 
3.2%
6381
 
1.9%
7235
 
1.2%
8165
 
0.8%
9127
 
0.6%
ValueCountFrequency (%)
411
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
292
< 0.1%
281
 
< 0.1%
251
 
< 0.1%
241
 
< 0.1%
232
< 0.1%
224
< 0.1%
213
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2385
Minimum0
Maximum41
Zeros1941
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:29.284081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum41
Range41
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.065776155
Coefficient of variation (CV)0.9228394705
Kurtosis25.84487648
Mean2.2385
Median Absolute Deviation (MAD)1
Skewness3.447847569
Sum44770
Variance4.267431122
MonotonicityNot monotonic
2021-11-29T11:24:29.367429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
16072
30.4%
26068
30.3%
32871
14.4%
01941
 
9.7%
41165
 
5.8%
5677
 
3.4%
6411
 
2.1%
7253
 
1.3%
8175
 
0.9%
9137
 
0.7%
Other values (22)230
 
1.1%
ValueCountFrequency (%)
01941
 
9.7%
16072
30.4%
26068
30.3%
32871
14.4%
41165
 
5.8%
5677
 
3.4%
6411
 
2.1%
7253
 
1.3%
8175
 
0.9%
9137
 
0.7%
ValueCountFrequency (%)
411
< 0.1%
331
< 0.1%
311
< 0.1%
281
< 0.1%
271
< 0.1%
261
< 0.1%
252
< 0.1%
241
< 0.1%
231
< 0.1%
222
< 0.1%

PTT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36965
Minimum0
Maximum44
Zeros15602
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:29.453551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum44
Range44
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.071525215
Coefficient of variation (CV)2.898756161
Kurtosis247.6400833
Mean0.36965
Median Absolute Deviation (MAD)0
Skewness10.20323425
Sum7393
Variance1.148166286
MonotonicityNot monotonic
2021-11-29T11:24:29.528301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
015602
78.0%
13069
 
15.3%
2696
 
3.5%
3291
 
1.5%
4124
 
0.6%
590
 
0.4%
646
 
0.2%
726
 
0.1%
820
 
0.1%
912
 
0.1%
Other values (12)24
 
0.1%
ValueCountFrequency (%)
015602
78.0%
13069
 
15.3%
2696
 
3.5%
3291
 
1.5%
4124
 
0.6%
590
 
0.4%
646
 
0.2%
726
 
0.1%
820
 
0.1%
912
 
0.1%
ValueCountFrequency (%)
441
 
< 0.1%
351
 
< 0.1%
231
 
< 0.1%
222
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
151
 
< 0.1%
143
< 0.1%
133
< 0.1%
123
< 0.1%

WBC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.00495
Minimum0
Maximum31
Zeros2000
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:29.609201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.703194217
Coefficient of variation (CV)0.8494946094
Kurtosis26.10013708
Mean2.00495
Median Absolute Deviation (MAD)1
Skewness3.395578474
Sum40099
Variance2.900870541
MonotonicityNot monotonic
2021-11-29T11:24:29.764818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26531
32.7%
16440
32.2%
32841
14.2%
02000
 
10.0%
41039
 
5.2%
5465
 
2.3%
6236
 
1.2%
7134
 
0.7%
8107
 
0.5%
975
 
0.4%
Other values (17)132
 
0.7%
ValueCountFrequency (%)
02000
 
10.0%
16440
32.2%
26531
32.7%
32841
14.2%
41039
 
5.2%
5465
 
2.3%
6236
 
1.2%
7134
 
0.7%
8107
 
0.5%
975
 
0.4%
ValueCountFrequency (%)
311
 
< 0.1%
301
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
221
 
< 0.1%
212
< 0.1%
202
< 0.1%
192
< 0.1%
181
 
< 0.1%
173
< 0.1%

Fibrinogen
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2106
Minimum0
Maximum29
Zeros18052
Zeros (%)90.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:29.849505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9096642317
Coefficient of variation (CV)4.319393313
Kurtosis150.6363715
Mean0.2106
Median Absolute Deviation (MAD)0
Skewness8.999016386
Sum4212
Variance0.8274890145
MonotonicityNot monotonic
2021-11-29T11:24:29.926165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
018052
90.3%
11080
 
5.4%
2352
 
1.8%
3206
 
1.0%
4110
 
0.5%
575
 
0.4%
649
 
0.2%
730
 
0.1%
821
 
0.1%
97
 
< 0.1%
Other values (11)18
 
0.1%
ValueCountFrequency (%)
018052
90.3%
11080
 
5.4%
2352
 
1.8%
3206
 
1.0%
4110
 
0.5%
575
 
0.4%
649
 
0.2%
730
 
0.1%
821
 
0.1%
97
 
< 0.1%
ValueCountFrequency (%)
291
< 0.1%
281
< 0.1%
181
< 0.1%
171
< 0.1%
161
< 0.1%
151
< 0.1%
142
< 0.1%
132
< 0.1%
121
< 0.1%
111
< 0.1%

Platelets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0355
Minimum0
Maximum31
Zeros1992
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:30.011428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.759856416
Coefficient of variation (CV)0.8645818796
Kurtosis26.99818944
Mean2.0355
Median Absolute Deviation (MAD)1
Skewness3.497953295
Sum40710
Variance3.097094605
MonotonicityNot monotonic
2021-11-29T11:24:30.092396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26458
32.3%
16394
32.0%
32840
14.2%
01992
 
10.0%
41082
 
5.4%
5487
 
2.4%
6268
 
1.3%
7143
 
0.7%
8110
 
0.5%
983
 
0.4%
Other values (17)143
 
0.7%
ValueCountFrequency (%)
01992
 
10.0%
16394
32.0%
26458
32.3%
32840
14.2%
41082
 
5.4%
5487
 
2.4%
6268
 
1.3%
7143
 
0.7%
8110
 
0.5%
983
 
0.4%
ValueCountFrequency (%)
311
 
< 0.1%
301
 
< 0.1%
271
 
< 0.1%
252
 
< 0.1%
242
 
< 0.1%
222
 
< 0.1%
202
 
< 0.1%
191
 
< 0.1%
183
< 0.1%
175
< 0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:30.188965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:30.289053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Gender
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:30.396449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:30.498021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Unit1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct208
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.81
Minimum0
Maximum336
Zeros6095
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:30.604901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q343
95-th percentile56
Maximum336
Range336
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.20781453
Coefficient of variation (CV)0.977538774
Kurtosis22.40359888
Mean26.81
Median Absolute Deviation (MAD)21
Skewness2.856933293
Sum536200
Variance686.8495425
MonotonicityNot monotonic
2021-11-29T11:24:30.702918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06095
30.5%
39486
 
2.4%
36483
 
2.4%
41479
 
2.4%
40462
 
2.3%
38449
 
2.2%
43442
 
2.2%
37416
 
2.1%
42414
 
2.1%
44385
 
1.9%
Other values (198)9889
49.4%
ValueCountFrequency (%)
06095
30.5%
8145
 
0.7%
972
 
0.4%
1086
 
0.4%
1172
 
0.4%
1290
 
0.4%
13119
 
0.6%
14135
 
0.7%
15180
 
0.9%
16202
 
1.0%
ValueCountFrequency (%)
3362
< 0.1%
3352
< 0.1%
3341
< 0.1%
3331
< 0.1%
3271
< 0.1%
3261
< 0.1%
3201
< 0.1%
3102
< 0.1%
3071
< 0.1%
3001
< 0.1%

Unit2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct208
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.81
Minimum0
Maximum336
Zeros6095
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:30.807512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q343
95-th percentile56
Maximum336
Range336
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.20781453
Coefficient of variation (CV)0.977538774
Kurtosis22.40359888
Mean26.81
Median Absolute Deviation (MAD)21
Skewness2.856933293
Sum536200
Variance686.8495425
MonotonicityNot monotonic
2021-11-29T11:24:30.905034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06095
30.5%
39486
 
2.4%
36483
 
2.4%
41479
 
2.4%
40462
 
2.3%
38449
 
2.2%
43442
 
2.2%
37416
 
2.1%
42414
 
2.1%
44385
 
1.9%
Other values (198)9889
49.4%
ValueCountFrequency (%)
06095
30.5%
8145
 
0.7%
972
 
0.4%
1086
 
0.4%
1172
 
0.4%
1290
 
0.4%
13119
 
0.6%
14135
 
0.7%
15180
 
0.9%
16202
 
1.0%
ValueCountFrequency (%)
3362
< 0.1%
3352
< 0.1%
3341
< 0.1%
3331
< 0.1%
3271
< 0.1%
3261
< 0.1%
3201
< 0.1%
3102
< 0.1%
3071
< 0.1%
3001
< 0.1%

HospAdmTime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:31.009446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:31.110244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:31.294321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:31.394516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

SepsisLabel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:31.503616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:31.604168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Sepsis
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:31.711086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:31.811692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:31.919006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:32.019279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Interactions

2021-11-29T11:24:21.721428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:09.464430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:09.771660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.042799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.316423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.648853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.920743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.193908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.466382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.739971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.012941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.307101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.595147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.938846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.207895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.478428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.748884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.018795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.287332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.560597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.912686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.173422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.446036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.715443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.001727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.273557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.546355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.892582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.177718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.453483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.739862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.998223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.257164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.513435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.782130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.129155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.402914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.681430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.962072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.241094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.519836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.802703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.158042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.440846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.815424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:09.568495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:09.862953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.134631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.403692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.740219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.012401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.285341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.558615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.832094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.111778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.403764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.760149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.029091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.298799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.569041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.839390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.109246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.379129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.653762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.000524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.265152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.536305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.811733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.093260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.365082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.637544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.988055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.270375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.549140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.827012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.086247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.343403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.603481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.873600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.220531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.496502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.776002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.055807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.334813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.614875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.896785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.252999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.534956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.909029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:09.670369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:09.953113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.225672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.490048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:10.830685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.103549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.375980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.649169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:11.922725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.209664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.499312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:12.849685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.118573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.388835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.659231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:13.929245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.198485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.469810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:14.820410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.086951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.355778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.625828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:15.906601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.183604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.455539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:16.727964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.083270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.361964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.643888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:17.912724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.172021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.428377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:18.693154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.038913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.312188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.588865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:19.869227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.148518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.427502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.708842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:20.989694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.346867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:21.628316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:24:32.165368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:24:32.509112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:24:32.927253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Missing values

2021-11-29T11:24:22.154206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:24:23.253842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
01000012222522222222000000001010105020200110101242424242424242424
1100002232323232323233000000010101012022301110101252525252525252525
2100003363612353235320000000232303280323232202024343004343434343
310000457561455555550000000000000000000000000000595959595959595959
4100005444419434143420001222151705011241714553303525252525252525252
510000627278272527270020022131303080215105513034747004747474747
610000736369363636340000000121202130112105511013737003737373737
7100008464540464646421300655006011560235541100330303505050505050505050
81000092626624242480000000010101010101001101013030003030303030
91000101515315151515000000001010109011100000000161616161616161616

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
19990119991232223232323193000000010101011010100110101252525252525252525
1999111999240401140404039000000001010106011101110101414141414141414141
1999211999377663680000000010101010111021101012121002121212121
19993119994414141414141409005666030703024631800220202424242424242424242
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